Disturbance suppression via robust MPC using prior disturbance data application to flight controller design for Gust Alleviation

Abstract

This paper addresses disturbance suppression problem for uncertain plant systems using prior disturbance data which contain some measurement errors. We tackle optimal control input design problem using Model Predictive Control (MPC) scheme in which a priori measured disturbance data are exploited. We show that if the uncertainties of the plant systems are expressed by bounded but time-invariant uncertain delays at the control input, then we only have to consider finitely many plant models instead of the original uncertain plant systems. Furthermore, we also show that if the measurement errors in prior disturbance data are expressed as affine with respect to some constant uncertain vector, whose elements are bounded, then we only have to evaluate the measurement errors at the vertices of the vector. Using these, we propose a robust MPC design with finitely many conditions for our addressed problem. Finally, we apply our proposed method to flight controller design problem for suppressing the vertical acceleration driven by turbulence, i.e. Gust Alleviation (GA) flight controller design problem.